U.S. patent number 6,175,772 [Application Number 09/059,278] was granted by the patent office on 2001-01-16 for user adaptive control of object having pseudo-emotions by learning adjustments of emotion generating and behavior generating algorithms.
This patent grant is currently assigned to Yamaha Hatsudoki Kabushiki Kaisha. Invention is credited to Tsuyoshi Kamiya, Masaya Sakaue.
United States Patent |
6,175,772 |
Kamiya , et al. |
January 16, 2001 |
User adaptive control of object having pseudo-emotions by learning
adjustments of emotion generating and behavior generating
algorithms
Abstract
A control method for controlling operation of an object used by
a user in an environment includes the steps of: defining
pseudo-emotions of the object for deciding output of the object, in
relation to the user's state; formulating emotion generation
algorithms to establish the relationship between the user's state
and the pseudo-emotions; formulating behavior decision algorithms
to establish the relationship between input, including the
pseudo-emotions, and the behavior of the object; detecting the
user's state; generating a pseudo-emotion of the object based on
the user's state using the emotion generation algorithms; making
the object behave based on the user's state and the pseudo-emotion
using the behavior decision algorithms; evaluating reaction of the
user in response to the behavior of the object; and if the reaction
of the user does not match the pseudo-emotion of the object in the
emotion generation algorithms, adjusting at least either of the
emotion generation algorithms or the behavior decision algorithms,
followed by learning the adjustment. The object can detect the
user's state in a visual, tactile, and auditory manner as do
humans, and can act upon generation of pseudo-emotions based
thereon. Thus, natural communication between the user and the
object can be performed, i.e., more human like communication can be
established.
Inventors: |
Kamiya; Tsuyoshi (Iwata,
JP), Sakaue; Masaya (Iwata, JP) |
Assignee: |
Yamaha Hatsudoki Kabushiki
Kaisha (Shizuoka-ken, JP)
|
Family
ID: |
14080711 |
Appl.
No.: |
09/059,278 |
Filed: |
April 13, 1998 |
Foreign Application Priority Data
|
|
|
|
|
Apr 11, 1997 [JP] |
|
|
9-093380 |
|
Current U.S.
Class: |
700/31;
701/1 |
Current CPC
Class: |
F24F
11/62 (20180101); F24F 11/30 (20180101); G06N
3/008 (20130101); Y02B 30/70 (20130101); F24F
2120/20 (20180101); F24F 2110/20 (20180101); F24F
2110/50 (20180101); F24F 2110/22 (20180101); F24F
2110/10 (20180101); F24F 2110/32 (20180101); F24F
2120/10 (20180101); F24F 2110/12 (20180101) |
Current International
Class: |
G06N
3/00 (20060101); G06F 015/18 () |
Field of
Search: |
;700/245,1,19,259,31
;701/1,59 ;382/104 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Lee; Thomas C.
Assistant Examiner: Wang; Albert
Attorney, Agent or Firm: Knobbe, Martents, Olson & Bear,
LLP
Claims
What is claimed is:
1. A control method for controlling operation of an object used by
a user in an environment, said object capable of receiving signals
of variable conditions which represent at least a state of the user
and which are associated with operation of the object, said object
capable of being programmed to behave in response to the received
signals, and method comprising the steps of:
(a) defining pseudo-emotions of the object, which are elements for
deciding output of the object, in relation to the signals;
(b) formulating emotion generation algorithms to establish the
relationship between the signals and the pseudo-emotions;
(c) formulating behavior decision algorithms to establish the
relationship between input, including the pseudo-emotions, and the
behavior of the object;
(d) detecting signals of variable conditions and inputting the
signals into the object;
(e) generating a pseudo-emotion of the object based on the signals
using the emotion generation algorithms;
(f) determining the variable conditions from the signals;
(g) making the object behave based on the variable conditions and
the pseudo-emotion using the behavior decision algorithms;
(h) evaluating reaction of the user in response to the behavior of
the object;
(i) if the reaction of the user does not match the pseudo-emotion
of the object in the emotion generation algorithms, adjusting and
updating at least either of the emotion generation algorithms or
the behavior decision algorithms, followed by learning the
adjustment; and
(j) repeating steps (d) through (i).
2. The control method according to claim 1, further comprising the
steps of: recognizing an intention/emotional expression of the user
based on the signals of variable conditions, and using the
intention/emotional expression of the user as the signals for
formulating the emotion generation algorithms and for generating
the pseudo-emotion of the object.
3. The control method according to claim 1, further comprising the
steps of: recognizing an intention/emotional expression of the user
based on the signals of variable conditions, and using the
intention/emotional expression of the user as the input for
formulating the behavior decision algorithms and for deciding the
behavior of the object.
4. The control method according to claim 2, further comprising the
steps of: recognizing an intention/emotional expression of the user
based on the signals of variable conditions, and using the
intention/emotional expression of the user as the input for
formulating the behavior decision algorithms and for deciding the
behavior of the object.
5. The control method according to claim 2, further comprising the
steps of deducing preference/habit of the user from the recognized
intention/emotional expression of the user, learning the deduced
result, and using the learned deduced result for recognizing the
intention/emotional expression of the user.
6. The control method according to claim 3, further comprising the
steps of deducing preference/habit of the user from the recognized
intention/emotional expression of the user, learning the deduced
result, and using the learned deduced result for recognizing the
intention/emotional expression of the user.
7. The control method according to claim 3, wherein the behavior
decision algorithms output at least a decision of
target-achievement action corresponding to the recognized
intention/emotional expression of the user and a decision of
emotional behavior corresponding to the pseudo-emotion of the
object, wherein the order of priority is set for each decision
based on the pseudo-emotion of the object, and the behavior
decision algorithms output either decision based on the order of
priority.
8. The control method according to claim 1, wherein the signals of
variable conditions are detected using sense-based detecting
means.
9. The control method according to claim 1, wherein the signals of
variable conditions are selected from the group consisting of the
user's facial expression, gesture, manner of touching, and voice
condition.
10. The control method according to claim 1, wherein the emotion
generation algorithms are formulated using basic emotional models
corresponding to plural emotional patterns, and output at least one
emotional model as the pseudo-emotion.
11. The control method according to claim 10, wherein the emotion
generation algorithms are formulated to select one emotional model
from the basic emotional models based on the signals of variable
conditions, and to output the emotional model as the
pseudo-emotion.
Description
BACKGROUND OF THE INVENTION
This invention relates to a system for controlling an object
interacting with a user and environment, and particularly to that
for controlling an object capable of expressing pseudo-emotions in
response to the user or environment by using algorithms, thereby
creating behavior highly responsive to the states of the user or
environment.
Heretofore, various controlling methods have been available for
controlling an object in accordance with a user's demand.
In such controlling methods, normally, the user sets a target value
at output which the user wants, and the object is controlled in
such a way that the output matches the target value, while feeding
the output back to a control system which compares the feedback and
the target value to adjust the output. In the above, by feeding the
output back to the system to adjust the output, the output of the
object to be controlled can approach the target value, thereby
achieving control satisfying the user's preference.
The aforesaid target value is set at a value basically satisfying
the request by the user who uses the object. In practice, methods
of setting a target value include a method of setting an
appropriate target value by the user at every time the user uses
the object, e.g., setting a temperature of an air conditioner, and
a method of setting an appropriate target value by a manufacturer
to satisfy a wide range of users when the object is manufactured,
e.g., setting parameters of an engine for a vehicle when
manufactured.
However, in conventional methods, because a principle goal is to
obtain output in accordance with a target value, when the user
inputs a target value directly into an object, if an incorrect
target value is mistakenly inputted, for example, the object is
controlled based on the incorrect target value. Satisfactory
results cannot be obtained. In the above, no matter how accurately
the air conditioner controls the temperature, i.e., outputs the set
target value, the user often must reset the target temperature as
the user notices the temperature is not the one the user really
wants after the user feels the output temperature controlled by the
air conditioner. This is because it is difficult for the user to
precisely and numerically find the right temperature, and to input
the temperature value into the air conditioner.
Further, when the target value is set in advance by a manufacturer,
for example, because the user who uses the object has different
characteristics from other users, it is impossible to set in
advance a universal target which satisfies all users.
As descried above, in the conventional methods, because the
principle goal is to obtain output in accordance with a target
value which is set directly by the user or set in advance by the
manufacturer, the output is likely to be stable and predictable.
However, the output may not be the one the user wants, and may not
reflect the user's intent or emotions which are not directly
expressed or inputted.
In addition, in the conventional methods, because the principle
goal is to obtain output in accordance with a target value,
naturally, the output is likely to be predictable. Thus, if such a
control system is applied to a toy, for example, behavior of the
toy is inevitably restricted to predictable mechanical movement. As
a result, the user loses interest in or gets tired of playing with
the toy.
SUMMARY OF THE INVENTION
An objective of the present invention is to solve the above
problems associated with conventional control systems, and to
provide a control system which allows outputting an adequate value
ultimately giving the user more satisfaction than does a value
obtained from the user's direct order, particularly using
pseudo-emotions caused in the object in response to the user and
environment.
One important aspect of the present invention attaining the above
objective is to provide a control method for controlling operation
of an object used by a user in an environment, said object capable
of receiving signals of variable conditions which represent at
least a state of the user and which are associated with operation
of the object, said object capable of being programmed to behave in
response to the received signals, said method comprising the steps
of: defining pseudo-emotions of the object, which are elements for
deciding output of the object, in relation to the signals;
formulating emotion generation algorithms to establish the
relationship between the signals and the pseudo-emotions;
formulating behavior decision algorithms to establish the
relationship between input, including the pseudo-emotions, and the
behavior of the object; detecting signals of variable conditions
and inputting the signals into the object; generating a
pseudo-emotion of the object based on the signals using the emotion
generation algorithms; making the object behave based on the
signals and the pseudo-emotion using the behavior decision
algorithms; evaluating reaction of the user in response to the
behavior of the object; and if the reaction of the user does not
match the pseudo-emotion of the object in the emotion generation
algorithms, adjusting at least either of the emotion generation
algorithms or the behavior decision algorithms, followed by
learning the adjustment.
According to the present invention, the object is always allowed to
regulate to a certain degree its behavior based on its own
pseudo-emotions. The object is formed in such a way as to adjust
the emotion generation algorithms and the behavior decision
algorithms and to learn them based on the reaction by the user in
response to the object's own behavior, and thus, the recognition
efficiency of recognizing the user's intentions and/or emotions
increases. Further, the object can detect the user's state in
visual, tactile, or auditory manner as do humans, and can act upon
generation of pseudo-emotions based thereon. Thus, natural
communication between humans and objects can be performed, i.e.,
more human like communication can be established.
In the above, preferably, the control method further comprises the
steps of: recognizing an intention/emotional expression of the user
based on the signals of variable conditions, and using the
intention/emotional expression of the user as the signals for
formulating the emotion generation algorithms and for generating
the pseudo-emotion of the object. The intention/emotional
expression of the user can also be used as the input for
formulating the behavior decision algorithms and for deciding the
behavior of the object. In the above, preferably, the control
method further comprises the steps of deducing preference/habit of
the user from the recognized intention/emotional expression of the
user, learning the deduced result, and using the learned deduced
result for recognizing the intention/emotional expression of the
user.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a schematic view showing a communication partner robot to
which the control system according to the present invention is
adapted.
FIG. 2 is a schematic diagram showing the relationship between the
user and the robot.
FIG. 3 is a schematic diagram showing the structures of the
controller 20 illustrated in FIG. 1.
FIGS. 4a, 4b, 4c, 4d show the patterns of rubbing the robot at the
four electrostatic approach-sensing sensor units illustrated in
FIG. 1.
FIG. 5 shows a map for making output values of the detection unit
23 to correspond to five emotional models: neutral, happy,
disgusted, angry, and sad, among the aforesaid seven emotional
models.
FIG. 6 is a schematic diagram showing a neural network usable for
generating the pseudo-emotions at the pseudo-emotion generation
unit 32 illustrated in Figure, wherein the user's state, which is
recognized based on the user's facial expressions, gestures,
rubbing/hitting behavior, voices, or the like, is used as input,
and basic emotional models are outputted.
FIG. 7 is a schematic diagram showing an example of setting the
order of priority on each behavior based on the pseudo-emotion of
the robot.
FIG. 8 is a schematic diagram showing facial expression patterns
which may be indicated on the display 10 to express the
pseudo-emotion of the robot.
FIG. 9 is a schematic diagram showing a second embodiment of the
present invention wherein the control system of the present
invention is applied to a vehicle such as a two-wheeled
vehicle.
FIG. 10 is a schematic diagram showing the relationship between the
basic emotional models and the user's vehicle-operating state and
the vehicle's driving state.
FIG. 11 is a schematic diagram showing a controller 220 in a third
embodiment of the present invention, wherein the control system of
the present invention is applied to an air conditioner.
FIG. 12 is a schematic diagram showing the relationship between the
detection elements, the recognized user's state, and the generated
pseudo-emotion.
FIG. 3 is a schematic diagram showing the processing flow of
dissatisfaction detection with reference to dissatisfaction with
temperature control.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE
INVENTION
The control system for controlling an object using pseudo-emotions
of the present invention (hereinafter, referred to simply as
"control system") will be explained with reference to several
embodiments depicted in the Figures.
Basic Structures of Communication Partner Robot
FIG. 1 is a schematic view showing a communication partner robot
(hereinafter, referred to simply as "robot") to which the control
system according to the present invention is adapted, and FIG. 2 is
a schematic diagram showing the relationship between the user and
the robot.
This robot 1 generates its own pseudo-emotions based on the user's
conditions, user's intention or emotions, and the like, and
expresses the pseudo-emotions by several behavioral means. Based on
a response from the user in response to the expression of its own
pseudo-emotions, the robot determines whether the generated
pseudo-emotions are correct, and also determines whether the user
understands and recognizes correctly the behavior created by the
pseudo-emotions. In this way, the robot is controlled to undergo
learning adequate pseudo-emotions to be generated and adequate
behavior for expressing the pseudo-emotions and to evolve. By doing
the above, the robot can develop its own pseudo-emotions and
expression of the pseudo-emotions through communications with the
user.
Sensing Means
The robot 1 comprises a CCD camera 2 as a visual detection means, a
pressure-sensing sensor 4 and an approach-sensing sensor 6 as touch
detection means, and a microphone 8 as a hearing-detection means.
By using these sensing means 2, 4, 6, and 8, the robot 1 detects by
sense the state of the user, such as a tone of voice, facial
expressions, and gestures, and the operational environments where
the robot is used.
The CCD camera 2 is installed on the top of the head and can be set
in any direction via a universal joint. For example, the robot can
be controlled in such a way that the robot automatically moves
toward an object, such as a human and animal, which is a cause or
source of information such as changes in temperature and sound.
Image information such as facial expressions of the user and
surrounding environments is supplied to a controller 20.
The pressure-sensing sensor 4 may be installed in the lower front
of the robot 1 so that when the robot has actual contact with an
obstacle, such information is provided to the controller 20.
In addition, the approach-sensing sensor 6 is comprised of four
units located on the top of the robot's body (i.e., the back of the
robot 1 if the robot is considered to have a face, a head, a body,
legs or driving means). The four units are electrostatic
approach-sensing sensor units 6FL, 6FR, 6RL, and 6RR disposed in
the front left, front right, rear left, and rear right,
respectively. These sensors provide to the controller 20
information regarding the state of ON or OFF and the duration of
each ON and OFF.
The microphone 8 is installed on the side of the head of the robot
1, and provides information to the controller 20 upon collecting
sound/voices arose around the robot 1.
Behavioral Means
The robot 1 comprises a display 10 to display its own facial
expression and other specific information, a speaker 12 to output
words, notes, roars, or effective sounds, and a drive means 14 to
move in autonomic manner. By using the above, the robot 1 can act
in accordance with the user's intent as necessary, while expressing
its own pseudo-emotions.
Controller
As described above, the robot 1 generates its own pseudo-emotions,
recognizes the user's intent and desires (emotional expression),
and recognizes an operable range, by using the built-in controller
20, based on information regarding the user's state and/or
surrounding environments obtained by each of the sensing means 2,
4, 6, and 8. Based on the generated pseudo-emotions, actions of the
display 10, the speaker 12, and the drive means 14 are desired, and
the robot 1 starts an action to achieve the target in accordance
with the user's intent or starts emotional behavior giving priority
to expressed emotions.
In addition, the robot 1 is controlled by the controller 20 in such
a way as to evaluate adequacy of the generated pseudo-emotions and
adequacy of actions by detecting by sense the user's reaction
caused by its own behavior, and to learn the evaluation results,
thereby undergoing evolution.
Structures of Controller
The structures of the controller 20 will be explained further
below. FIG. 3 is a schematic diagram showing the controller 20.
The controller 20 receives information from the aforesaid sensuous
detection means 2, 4, 6, and 8, and information from an external
information source 9 such as a network as necessary.
The controller 20 comprises a facial expression detection unit 21,
a gesture detection unit 22, a rubbing/hitting detection unit 23, a
sound/voice detection unit 24, and a surrounding environment
detection unit 25, and by using these detection units 21-25, the
controller 20 detects information about the state of the user
and/or the surrounding environment.
In practice, the facial expression detection unit 21 and the
gesture detection unit 22 visually detect the user's expression
and/or gesture, using an appropriate image recognition system.
The rubbing/hitting detection unit 23 tactilely detects the user's
actions of rubbing and/or hitting the robot 1, based on input
information from the pressure-sensing sensor 4 and the
approach-sensing sensor 6.
Further, the rubbing/hitting detection unit 23 conducts two-step
detection: detection of contacted location and detection of being
rubbed, i.e., the unit 23 detects first whether the user rubs the
robot 1, and if the user rubs the robot, second how the user rubs
the robot 1. The detection of contacted location is conducted in
such a way that among the electrostatic approach-sensing sensors,
if either sensor unit 6FL (front left) or 6FR (front right) is ON,
the contact is judged to be located in the front; if either sensor
unit 6RL (rear left) or 6RR (rear right) is ON, the contact is
judged to be located in the rear; if either sensor unit 6FL (front
left) or 6RL (rear left) is ON, the contact is judged to be located
on the left; if either sensor unit 6FR (front right) or 6RR (rear
right) is ON, the contact is judged to be located on the right,
thereby detecting which part of the approach-sensing sensor 6 is
touched by the user. Rubbing detection is established when the
contacted location moves from the front to the rear, from the right
to the left, or the like. FIGS. 4a, 4b, 4c, and 4d show the
patterns of rubbing the robot 1 at the four electrostatic
approach-sensing sensor units. In the above, in rubbing detection,
a manner of rubbing by the user is detected based on the duration
of activation of each sensor unit: how long each sensor unit is
kept ON or OFF. For example, when the user does not move the hand
touching the sensor unit(s) for a long time, it is judged that the
robot 1 is being held (FIG. 4c); when the user rubs right and left
at appropriate intervals without releasing the hand, it is judged
that the robot 1 is being rubbed (FIG. 4a); when the user rubs in
one direction as if rubbing along fur and releases the hand when
returning, it is judged that the robot 1 is being rubbed (FIG. 4d);
when any sensor unit is activated (an ON signal is detected) once
for a very short time while all of the sensor units are OFF, it is
judged that the robot is suddenly being hit by the user; when ON
signals are detected continuously at a certain interval, it is
judged that the robot 1 is being hit repeatedly. Further, in
addition to the approaching-sensing sensor 6, by installing a
pressure-sensing sensor at the same location as the
approach-sensing sensor 6, it can be detected whether the user
touches the robot 1 softly or roughly, or how strong the user hits
the robot 1 if hitting the robot 1.
The sound/voice detection unit 24 auditorily detects the user's
voice and/or sounds around the robot, based on information from the
microphone 8, and analyzes or recognizes voice information, using
an appropriate voice recognition means: e.g., recognition of the
contents of a conversation by the user, identification of the
individual user, or identification of sound around the robot.
Further, the surrounding environment detection unit 25 tactually
recognizes information about obstacles around the robot based on
input information from the pressure-sensing sensor 4, and visually
recognizes information about the obstacle by an appropriate image
recognition means based on input image information from the CCD
camera 2.
Recognition Units
The controller 20 generates pseudo-emotions at a pseudo-emotion
generation unit 32 upon recognition of the user's intent and
emotional expression at an intent/emotion recognition unit 31 based
on the detection results by the aforesaid detection units 21-25.
The controller 20 also recognizes an actually movable range of the
robot 1 at a movable range recognition unit 33.
Intent/Emotion Recognition Unit
In practice, the aforesaid intent/emotion recognition unit 31 may
comprise a neural network, for example, which receives information
about the user's preferences and habits obtained from a
preference/habit learning unit 35 described later, as well as
information about the state of the user obtained from the facial
expression detection unit 21, the gesture detection unit 22, and
the sound/voice detection unit 23, and which outputs the user's
intention/emotional expression based on the input information,
thereby recognizing information about the user's
intention/emotional expression based on the output value. Further
in practice, when the aforesaid intention/emotion recognition unit
31 is comprised of a neural network(s), the neural network can be
constructed in such a way as to allow selective recognition of the
user's emotional expression by causing the neural network to
undergo learning in advance, for example, the relationship between
several patterns of emotions and information including the user's
preference/habits and the state of the user. The several patterns
of emotions are obtained by categorizing in advance the emotional
expressions of the user into several patterns of emotions such as
neutral, disgusted, happy, sad, surprised, angry, and fearful. The
information on the state of the user is obtained from the facial
expression detection unit 21, the gesture detection unit 22, and
the sound/voice detection unit 23, and the information on the
user's preferences and habits is obtained from the preference/habit
learning unit 35.
Regarding recognition of the user's intentions using a neural
network, selective recognition of the user's intentions can be
established by causing the neural network to undergo advance
learning the relationship between pieces of information including
the user's state and preferences/habits, and the contents of
intentions. The contents of intentions, which are learned in
advance by the neural network, are those the user possibly requests
to the robot 1 such as "stop", "run", and "return".
The information on the user's intentions and/or emotional
expressions recognized at the intention/emotional expression
recognition unit 31 is used as one piece of information for
generating pseudo-emotions and as information for evaluating
adequacy of the generated pseudo-emotions at the pseudo-emotion
generation unit 32, and is used as information for creating
target-achievement motion in accordance with the user's
intentions/emotional expressions and as information related to
reaction from the user in response to the robot's behavior at a
behavior decision means 40, and is further used as teacher data
related to the user's preferences/habits at the preference/habit
learning unit 35.
The intention/emotional expression recognition unit 31 may be
constituted to learn and adjust recognition algorithms in such a
way as to eliminate a discrepancy between the estimated response
from the user in response to the robot's own behavior which is
originally a response to the state of the user, and the user's
actually evaluated intentions and/or emotional expressions.
Further, preferably, the patterns of emotional expressions and the
contents of the user's intentions may be recognized by subdividing
the patterns (e.g., disgusted and happy) and the contents (e.g.,
"run" and "stop") into several levels. In the above, when
recognized by subdividing the user's intentions and/or emotional
expressions, adjustment of the recognition algorithms can be
achieved in the subdivided levels of each of the patterns and the
contents of intentions. In practice, for example, when the user
orders the robot 1 to "run", the robot 1 recognizes the user's
intention "run" in level 2 (e.g., run normally) based on various
states such as the facial expression of the user when saying "run",
thereby behaving accordingly. As a result, if the robot 1
recognizes that the user's response shows dissatisfaction, the
robot judges that there is a discrepancy between the user's
recognized intention and the user's actual intention, and the robot
changes the outcome of the recognition of the user's intention to
the level where the user is judged to be satisfied (e.g., level 3:
run fast, or level 1: run slowly), thereby learning the result. By
constructing the structure as above, the robot 1 is able to
implement the user's order, e.g., "run", by further recognizing the
contents which the user does not expressly order, e.g., the speed
or a manner of running.
In the above, the recognition at the intention/emotional expression
recognition unit 31 need not be constructed by a neural network,
but can be constructed by other means such as a map which can make
various states of the user detected by the detection units
correspond to the categorized emotional patterns.
Pseudo-Emotion Generation Unit
The pseudo-emotion generation unit 32 stores basic emotional models
obtained by categorizing pseudo-emotions required of the robot 1
into several emotional patterns which are, in this embodiment,
neutral, disgusted, happy, sad, surprised, angry, and fearful. The
pseudo-emotion generation unit 32 selectively generates a
pseudo-emotion from the above seven basic emotional models in
accordance with the current situation, by using a map or a function
which connects information on the user's state, information on the
user's intentions/emotional expressions, and the aforesaid each
emotional model, or by using a neural network which has learned the
relationship between information on the user's state, information
on the user's intention/emotional expression, and the aforesaid
each emotional model.
For example, FIG. 5 shows a map for making output values of the
detection unit 23 to correspond to five emotional models: neutral,
happy, disgusted, angry, and sad, among the aforesaid seven
emotional models. In the map, values obtained from the detection
unit 23 are distributed in the vertical axis "pleasant-unpleasant"
and in the horizontal axis "sleeping-busy" under the conditions
that if rubbed continuously, it means "pleasant", if held in place,
it means "unpleasant", if touched often, it means "busy", and if
left alone, it means "sleeping". According to this map, for
example, if the user continuously rubs the robot 1, the robot 1
displays happiness as its pseudo-emotion, if the user holds the
robot 1 in place a few times, the robot 1 displays disgust as its
pseudo-emotion, if the user holds the robot 1 in place many times,
the robot 1 displays anger as its pseudo-emotion, and accordingly,
the pseudo-emotions are generated which can correspond to
expressions or behavior of dislike and expressions or behavior of
joy generally found in animals including humans.
In the above, in addition to the corresponding relationship between
the output information of the rubbing/hitting detection unit 23 and
the emotional models, the information on the state of the user or
the information on the user's intention/emotional expression from
the other detection units 21, 22, and 24 can be used in combination
with the relationship. For example, even if the manner of rubbing
is in the disgusted range, if the user's state is judged to be good
based on the facial expression detection unit 21 and/or the
sound/voice detection unit 24, it is judged that the user is simply
playing with the robot and holding the robot, the ultimate
pseudo-emotion of the robot remains neutral, not disgusted.
Accordingly, the map, which makes the information on the user's
state and the information on the user's intention/emotional
expression correspond to the seven basic emotional models, can be
constructed.
In the above, if generation of the pseudo-emotions at the
pseudo-emotion generation unit 32 is conducted using a neural
network, as shown in FIG. 6, the user's state (such as laughing or
angry) recognized based on the user's facial expressions, gestures,
rubbing/hitting behavior, voices, or the like is used as input, and
basic emotional models are outputted (six models in FIG. 6). The
neural network is made to learn in advance the relationship between
such input and output, and to output, as parameters, coupling loads
wn (n=1-6) of each basic emotional model corresponding to the
user's state. Based on the coupling load of each basic emotional
model, an ultimate pseudo-emotion is determined using, for example,
a multi-dimensional map.
As described above, if the pseudo-emotion generation unit 32 is
formed by a control logic capable of learning, the pseudo-emotion
generation unit 32 may be constructed so as to recognize the user's
reaction in response to robot's own behavior based on the
information obtained from the intention/emotional expression
detection unit 31; to evaluate adequacy of the pseudo-emotion
generated based on the user's reaction; and if the generated
pseudo-emotion is judged to be unnatural, to adjust the
relationship between input information and output information in
such a way as to generate a natural pseudo-emotion in accordance
with the user's state and/or the user's intention/emotional
expression at the moment; and to thereby learn the results of
adjustment.
Preferably, this pseudo-emotion generation unit 32 can generate the
pseudo-emotions by subdividing each of the aforesaid seven basic
emotional models into several levels. For example, the happy model
can be subdivided into several levels to generate pseudo-emotions
containing the degree of happiness (e.g., a degree from "very
happy" to "slightly happy"). In this case, the pseudo-emotion
generation unit 32 may be formed so as to adjust pseudo-emotion
generation algorithms per subdivided level and to undergo learning
accordingly.
The information on the generated pseudo-emotions is outputted to
the behavior decision means 40, and used, at the behavior decision
means 40, as information for performing emotional behaviors, as
standard information for putting priority to several behaviors as
described later, and as comparison information for evaluating the
information related to the user's reaction obtained from the
intention/emotional expression recognition unit 31.
Ambient Environment Memory Unit and Movable Range Recognition
Unit
An ambient environment memory unit 34 successively memorizes
information related to the ambient environment inputted from the
ambient environment detection unit 25. A movable range recognition
unit 33 recognizes the range where the robot 1 can actually move
based on the information from the ambient environment detection
unit 25 and the ambient environment memory unit 34.
The information related to the recognized movable range is
outputted to the behavior decision unit 40.
Preference/Habit Learning Unit
The preference/habit learning unit 35 constantly receives
information related to the user's intentions/emotional expressions
recognized at the intention/emotional expression recognition unit
31, determines the user's preferences and/or habits based on the
user's intentions and/or emotional expressions and undergoes
learning the same, and outputs the information to the
intention/emotional expression recognition unit 31 as one piece of
information for recognizing the user's intentions/emotional
expressions.
In addition, the output from the preference/habit learning unit 35
is also inputted into an information-searching unit 36.
Information-Searching Unit, Information Memory Unit, Information
integration/Processing Unit
The information-searching unit 36 searches for adequate information
for the user's preference/habits obtained at the preference/habit
learning unit 35 using the external information source 9, and makes
the information memory unit 37 memorize the result. An information
integration/processing unit 38 integrates the searched information
and the memorized information, processes the information (e.g.,
selects necessary information), and outputs the result to the
behavior decision means 40.
Behavior Decision Means
As described above, all of the information related to the user's
intention/emotional expression recognized at, for example, each
recognition unit and each generation unit, the information related
to the pseudo-emotions of the robot, the information related to the
movable range, and the information related to the user's
intentions/emotional expressions are used as standards for
target-achievement action, the information related to the
pseudo-emotions of the robot is used as standards for emotional
behaviors, and the information related to the movable range is used
as standards for obstacle-avoiding action.
The behavior decision means 40 comprises a behavioral type
artificial intelligence system, and is formed by an appropriate
growth system which evaluates discrepancy between the
pseudo-emotions actually arising in the robot 1 and the
pseudo-emotions of the robot 1 recognized by the user, and makes
the behavioral type artificial intelligence system undergo
adjustment, learning, and/or evolving.
In practice, the behavior decision means 40 decides which actions,
i.e., target-achievement action, emotional behavior, or
obstacle-avoiding action, or wandering when no order is given, the
robot 1 is made to activate, by setting the order of priority on
each behavior.
In practice, the target-achievement action includes an action
directly in accordance with the target the user intends, i.e., an
action such that when the user orders "run", the robot "runs".
The emotional behavior includes actions basically residing in
emotions, such as an expression of happiness by indicating a
smiling face on the display 10 while displaying dancing behavior by
going back and forth or turning around using the drive means 14
when the pseudo-emotion is "happy", and an expression of anger by
indicating a angry face on the display 10 while rushing straight
using the drive means 14 when the pseudo-emotion is "angry".
Further, the object-avoiding action includes an action to avoid an
obstacle, and the wandering when no order is given includes a
repeating action such as going forward or changing directions
without targets.
Setting the order of priority on each behavior (i.e., emotional
behavior, target-achievement action, obstacle-avoiding action, and
wandering) is conducted based on the pseudo-emotion of the robot 1
generated at the pseudo-emotion generation unit 32.
FIG. 7 is a schematic diagram showing an example of setting the
order of priority on each behavior based on the pseudo-emotion of
the robot. As shown in this figure, in this embodiment, when the
pseudo-emotion is "neutral", the obstacle-avoiding action takes
priority over the other actions, followed by the target-achievement
action, i.e., the order of priority is set in order to make the
robot act as an obedient robot, which suppresses robot's own
pseudo-emotional drive, toward the user. When the pseudo-emotion is
"disgusted", "happy", or "sad", the obstacle-avoiding action takes
priority over the other actions, followed by the emotional
behavior, i.e., the order of priority is set in order to allow the
robot to express its own pseudo-emotions by, e.g., going away angry
or being sad due to the robot's action which was adverse to the
user's intention, or treading on air due to happiness. Further,
when the pseudo-emotion is "angry", "surprised", or "fearful", the
emotional behavior takes priority over the other actions, i.e., the
order of priority is set in order to allow the robot to express its
own pseudo-emotions such as an surprised expression when the robot
keeps going forward even if the robot hits an obstacle.
After completion of selection of behavior based on the above order
of priority, the behavior decision means 40 operates the display
10, the speaker 12, and the drive means 14 in order to take action
suitable for the contents of behavior selected by the robot.
In practice, the display 10 selects a facial expression suitable
for the pseudo-emotion of the robot at the moment among several
facial expression patterns such as those indicated in FIG. 8, and
displays the selected facial expression. In addition, when the user
requests the display of certain information, the display 10 can
display the information by request instead of the facial
expressions, or both the information and the facial
expressions.
The speaker 12 outputs a voice suitable for the pseudo-emotion of
the robot at the moment (such as laughing sounds when the
pseudo-emotion is "happy"), an answer in response to the user's
request when the target-achievement action is given priority, or
adequate effective sounds, by synthesizing appropriate sounds.
The drive means 14 drives according to the behavior taking
priority.
In the above, a decision method will be explained in detail when
the behavior decision means 40 selects emotional behavior. The
behavior decision means 40 learns in advance the relationship
between each of the basic seven emotional models (preferably each
level of each model) and plural patterns of behavior corresponding
thereto. For example, when the pseudo-emotion of the robot becomes
"angry" as a result of a situation where the user fixedly touches
the approach-sensing sensor 6 and maintains the situation, the
display 10 is made to display the facial expression of "angry" and
the drive means 14 is made to go back to escape from the user's
hand and to stop when the hand is detached from the sensor. When
the pseudo-emotion is "happy", the display 10 is made to display
the facial expression of "smiling face" and the drive means 14 is
made to move in combination of going back and forth and turning
around.
Learning at Behavior Decision Means
The behavior decision means 40 judges how the user recognizes the
pseudo-emotion of the robot 1, which was aroused in response to the
user's reaction which arose in response to robot's own behavior,
based on the information obtained from the intention/emotional
expression recognition unit 31, and the behavior decision means 40
evaluates the behavior decision algorithms at the behavior decision
unit 40, based on the discrepancy between the pseudo-emotion
recognized by the user and the pseudo-emotion actually aroused in
the robot.
If no discrepancy is found, the behavior decision unit 40 is judged
to optimally act expressing the pseudo-emotions, and the behavior
decision algorithms remain the same. If the actual pseudo-emotion
of the robot and the pseudo-motion recognized by the user are
different, the discrepancy is determined, and the relationship
between the pseudo-emotion and the behavior in the behavior
decision algorithms at the behavior decision means 40 is adjusted
to eliminate the discrepancy. For example, despite the
pseudo-emotion of anger aroused in the robot 1, the user recognizes
that the pseudo-emotion is "disgusted".
In the pseudo-emotions, for example, each of the basic seven
emotional models is subdivided into levels 1 to 3, and the behavior
decision algorithms have learned the relationship between the
emotional model "joyful" and the behavior as follows:
(1) "Joyful level 1 (slightly joyful)=going around with a smiling
face."
(2) "Joyful level 2 (joyful)=dancing around while laughing with a
smiling face."
(3) "Joyful level 3 (very joyful)=dancing around while loudly
laughing with a smiling face."
If the robot recognizes based on the user's reaction "the user
recognizes that the robot is very joyful (the pseudo-emotion:
joyful level 3)" as a result of behavior expressing "joyful level
2" by the robot, the behavior decision means 40 learns the
relationship between the emotional model "joyful" and the behavior
in order to match the pseudo-emotion of the robot being recognized
by the user and the pseudo-emotion aroused in the robot as
follows:
(1') "Joyful level 1 (slightly joyful)=running around with a
smiling face."
(2') "Joyful level 2 (joyful)=dancing around with a smiling
face."
(3') "Joyful level 3 (very joyful)=dancing around while laughing
with a smiling face."
As described above, by adjusting the relationship between the
pseudo-emotion and the behavior in the behavior decision algorithms
based on the reaction by the user, appropriate emotional behavior
corresponding to the pseudo-emotions can be established.
Effects Exhibited in The Embodiment
As explained above, because the robot 1 sets the order of priority
on several behavior patterns based on the pseudo-emotions of the
robot, the robot 1 is always allowed to regulate to certain degree
its behavior based on its pseudo-emotions, not only when the
emotional behavior takes priority over the other behaviors.
The robot 1 described above is formed in such a way as to adjust
the user's intention/recognition algorithms, emotion generation
algorithms, and behavior decision algorithms and to learn them
based on the reaction by the user in response to the robot's own
behavior, and thus, the recognition efficiency of recognizing
intentions/emotions increases, thereby establishing pseudo-emotions
and emotional expressions.
The robot 1 described above detects the user's state in visual,
tactile, and auditory manner as do humans, and acts upon generation
of pseudo-emotions based thereon. Thus, natural communication
between humans and robots can be performed, i.e., more human like
communication can be established.
Further, the robot 1 sets the order of priority on behaviors based
on its pseudo-emotions. Thus, the robot sometimes acts entirely
unexpectedly, and will not make the user tired of playing with the
robot.
Second Embodiment: Vehicle
FIG. 9 is a schematic diagram showing a second embodiment of the
present invention wherein the control system of the present
invention is applied to a vehicle such as a two-wheeled
vehicle.
In this embodiment, the user's state is detected based on the
driving state detected by, for example, a throttle sensor, a brake
sensor, or a sensor sensing handle bar operation (hereinafter,
referred to as "handle bar sensor").
In practice, based on detection information from the throttle
sensor, information related to the user's throttle operation is
detected; based on detection information from the brake sensor,
information related to the user's brake operation is detected; and
based on detection information from the handle bar sensor,
information related to the user's handle bar operation is
detected.
An intention/emotional expression recognition unit 131 may comprise
a neural network, for example, which receives information about the
user's preference and habits obtained from a preference/habit
learning unit 134, as well as information related to throttle
operation, brake operation, and handle bar operation (hereinafter,
these pieces of information are referred to as "information related
to a vehicle-operating state"), and which recognizes the user's
intentions/emotional expressions based on the user's
vehicle-operating state and the user's preference/habits. The basic
principle of the intention/emotional expression unit 131 is the
same as in the intention/emotional expression unit 31 in the first
embodiment, and thus, its detailed explanation will be omitted.
A pseudo-emotion generation unit 132 stores basic emotional models
obtained by categorizing pseudo-emotions required by the vehicle
into several emotional patterns which are, for example, neutral,
disgusted, happy, sad, surprised, angry, and fearful. The
pseudo-emotion generation unit 132 receives information related to
the user's vehicle-operating state as well as information related
to vehicle's state itself such as velocity, engine r.p.m's., engine
temperature, and the remaining fuel volume, and generates its own
pseudo-emotion using a neural network or a map which has learned
the relationship between the input information and the aforesaid
basic emotional models.
The relationship between the basic emotional models and the user's
vehicle-operating state and the vehicle's driving state itself is
determined by using as standards, for example, the changes in
velocity, the throttle-operating state, the throttle angle, the
acceleration state, the brake-operating state, and the changes in
the handle bar angle and by making them correspond to the basic
emotional models, as shown in FIG. 10. In the relationship
indicated in FIG. 10, it is judged that the higher the indices of
the speed change, the throttle-operating state, the throttle angle,
the accelerating state, and the brake-operating state, the rougher
the driving operation by the user becomes, whereby the basic
emotional model becomes "angry". Also, it is judged that the higher
the indices of the brake-operating state, the handle bar-operating
state, and the speed change, the higher the degree of the user's
tiredness becomes, whereby the basic emotional model becomes
"sad".
Preference/Habit Learning Unit and Other Units
The basic principles of a preference/habit learning unit, an
information-searching unit, an information memory unit, and an
information integration/processing unit are the same as in the
first embodiment, and thus, detailed explanation will be
omitted.
Behavior Decision Means
A behavior decision means 140 receives information related to the
user's intentions/emotions obtained at the intention/emotional
expression unit 131 as standards for target-achievement action, and
receives information related to the pseudo-emotions of the vehicle
as standards for emotional behavior, and gives priority to the
target-achievement action and the emotional behavior based on the
aforesaid pseudo-emotion, thereby determining either the
target-achievement action or the emotional behavior, based on the
order of priority.
In the above, for example, the target-achievement action includes
an action which simply targets the user's throttle operation, brake
operation, or handle bar operation, thereby operating the throttle
or the like.
Emotional behaviors include actions urging as priority the user to
take a rest using signals or voices when the pseudo-emotion is
"sad".
Practical operation means include driving operation subjected to
control related to driving performance of the vehicle such as
control over ignition timing, fuel flow, thermostat, ABS, TCS, and
suspension; an action of warning the user in a visual or auditory
manner, e.g., warning when driving at excessive speeds or when
running out of gas; and an action pf releasing the user's stress by
providing traffic information, weather reports, or news
flashes.
In addition, the operation means also include a means for
indicating the pseudo-emotion of the vehicle, wherein the user can
judge the vehicle's pseudo-emotions and can operate the vehicle in
such a way as to make the vehicle happy, thereby performing
adequate driving.
The intention/emotional expression unit 131, the pseudo-emotion
generation unit 132, and the behavior decision means 140 adjust, as
does the controller in the first embodiment, the user's
intention/recognition algorithms, emotion generation algorithms,
and behavior decision algorithms and learn the adjustments, using
the user's reaction in response to its behavior as evaluation
standards, and thus, the recognition efficiency of recognizing
intentions/emotions increases, thereby establishing its
pseudo-emotions and emotional expressions.
Third Embodiment: Air Conditioner
FIG. 11 is a schematic diagram showing a controller 220 in a third
embodiment of the present invention, wherein the control system of
the present invention is applied to an air conditioner.
In this embodiment, the controller 220 detects the user's state and
surrounding environment, and based on the detected information,
generates the pseudo-emotion of the air conditioner itself, and
recognizes the user's intentions/emotional expressions.
The air conditioner comprises, for example, a remote controller for
switch operation, a microphone, a CCD camera, and a pyroelectric
sensor as means for detecting information related to a state of the
user(s), and a clock, an indoor environment sensor (detecting, for
example, temperature, humidity, and degree of cleanness of air),
and an outdoor environment sensor (detecting, for example,
temperature, humidity, barometric pressure, wind velocity,
sunshine, rainfalls, and snowfalls) as means for detecting using
environment, and further, an external information source such as a
network.
The controller 220 detects intentions of user(s) based on
information obtained form the external information source, the
operation switch remote controller, and the microphone; detects
voices of the user(s) using an appropriate voice recognition means
based on information obtained from the microphone; detects sounds
made inside and outside the room including noises; detects facial
expressions and complexion of the user(s) based on information
obtained from the CCD camera; detects movement of user(s),
existence of user(s), and the number of user(s) based on
information obtained from the CCD camera and pyroelectric sensor;
detects indoor environment based on information obtained from the
indoor environment sensor; and detects outdoor environment or
weather based on information obtained from the outdoor environment
sensor.
An intention/emotional expression recognition unit 231 and a
pseudo-emotion generation unit 232 recognize intentions/emotional
expressions of the user(s) and generate pseudo-emotions of the air
conditioner itself, based on the detected state of the user(s) and
the detected using environment by using the aforesaid detection
means.
In practice, as shown in FIG. 12, if the pseudo-emotion generation
unit 232 for generating pseudo-emotions detects that the room is
quiet based on information from the microphone, detects no movement
by user(s) based on information from the proelectric sensor, and
detects that the room is dark based on information from the CCD
camera, and after the above conditions continue for a given time
period, if the proelectric sensor detects movement of user(s), the
CCD camera detects light, the microphone detects the sound of a key
in a door, sounds of a TV or radio, the sound of a curtain closing,
and/or sound of a playback of a voice mail device, then the
pseudo-emotion generation unit recognizes the arrival of user(s) at
home based on a combination in part of the foregoing detections.
Further, the pseudo-emotion generation unit recognizes the number
of user(s) who arrived using an appropriate image recognition means
based on information from the CCD camera, identifies the individual
user(s) using an appropriate voice recognition means based on
information from the microphone, recognizes whether the movement is
active or slow, recognizes the indoor environment such as room
temperature, humidity, sunniness, and tranquilness based on
information from the indoor environment sensor, and detects the
outdoor environment such as temperature and weather based on
information from the outdoor environment sensor, and based on the
foregoing recognized information, if, for example, the indoor and
outdoor temperatures are judged to be low at arrival of the
user(s), then the pseudo-emotion generation unit generates a
pseudo-emotion "want to warm up", and if the indoor and outdoor
temperature is judged to be low and outdoor winds are judged to be
strong at arrival of the user(s), then the pseudo-emotion
generation unit generates a pseudo-emotion "want to warm up
quickly".
The above-described pseudo-emotion generation can be achieved by
storing pseudo-emotion models required of the air conditioner, and
by causing a neural network to learn the relationship between the
pseudo-emotions and the various recognized data or by using a map
defining correspondence between the pseudo-emotions and the
recognized information.
In the above, the relationship between the pseudo-emotions and the
recognized information further includes, for example, "want to cool
down quickly" if it is hot when user(s) arrive(s), "want to provide
refreshing and dehumidified air" if it is hot and humid when
user(s) arrive(s), "want to prevent getting hot" if there are many
people or users, and "want to prevent getting chilled or too cold"
if it is quiet.
Like the pseudo-emotion generation unit 232, an intention/emotional
expression recognition unit 231 may comprise a neural network which
learns in advance the relationship between the recognized
information and the intentions/emotional expressions of the user(s)
to output to a behavior decision means 240 the intentions/emotional
expressions of the user(s) at the moment such as actual desire of
ventilation which is not expressly ordered by the user(s) but which
is recognizable based on information such as the user's complexion
related to feeling of the user, in addition to the particularly set
temperature controlled via the switch operation remote
controller.
In the above, the information on the intentions and emotional
expressions of the user(s) recognized at the intention/emotional
expression recognition unit 231 is also used as teacher data for a
preference/habit learning unit 235, and the preference/habit
learning unit 235 undergoes learning the preferences and/or habits
of the user(s) by adding them, and outputs the result of learning
to the intention/emotional expression recognition unit 231 as one
piece of the information used for recognizing the
intentions/emotional expressions of the user(s).
The behavior decision means 240 receives information related to the
pseudo-emotions of the air conditioner itself in addition to
information related to the recognized intention/emotional
expressions of the user(s); conducts control such as
cooling/heating capacity control, wind flow control, wind direction
control, air purifier control, and ventilation control based on a
control value which is obtained by summing a target-achievement
operation value based on the intentions/emotional expressions of
the user(s) and a pseudo-emotion operation value based on the
pseudo-emotions; and as necessary, informs the user(s) of
temperature and/or humidity by using a display or voice output.
Detection of Dissatisfaction and Learning
In addition, the intention/emotional expression recognition unit
231 also recognizes the intention/emotional expression of the
user(s) regarding dissatisfaction based on reaction by the user(s)
in response to the behavior or performance of the air conditioner
regulated by the aforesaid behavior decision means 240; and based
on the recognition result, adjusts the recognition algorithms at
the intention/emotional expression recognition unit 231 and the
behavior decision algorithms at the behavior decision means 240,
followed by learning the changes.
The dissatisfaction detection will be explained with reference to
dissatisfaction with temperature control. As shown in FIG. 13,
actions expressed when the user is dissatisfied with the
temperature, such as re-setting the remote controller, pronouncing
a word "hot" or "cold", and performing gestures such as fanning
himself or herself and shrinking, are detected by detection means
such as a operation switch, microphone, and CCD sensor; and
according to the detected information, the operation value is
recognized or the meaning of words is recognized by using an
appropriate voice recognition means. Further, behavior or
complexion of the user is detected using an appropriate image
recognition means, and if the user is judged to perform an action
observed when dissatisfied with temperature, the user's
dissatisfaction is established.
The controller adjusts the aforesaid recognition algorithms and the
behavior decision algorithms based on the detected dissatisfaction,
and learns the results, and also learns the user's
preferences/habits, whereby the recognition efficiency of
recognizing intentions/emotions increases, and its pseudo-emotions
and emotional expressions are becoming established.
Effects Exhibited in The Third Embodiment
As explained above, because the air conditioner recognizes the
intentions/emotional expression of the user(s) by taking the
preference/habits of the user(s) into account, temperature control,
for example, with consideration of each individual user's
preferences/habits, can be conducted for a user who has a habit to
always raising the set temperature from the temperature the user
actually desires, or a user who cannot tolerate the heat and
prefers a relatively low temperature. As a result, the air
conditioner allows providing satisfactory environment to the
user(s) whose unexpressed intentions/emotional expressions are
satisfied.
Further, according to the above third embodiment, because the air
conditioner is constructed so as to conduct, for example,
temperature control by adding the emotional behavior based on its
own pseudo-emotions to the target-achievement action based on the
user's intentions/emotional expressions, the air conditioner can
provide environment optimal to the state of the user(s) even if no
order is given.
Other Embodiments
In the first embodiment, the CCD camera, for example, is used as
means for detecting the state of the user(s) or the using
environment. However, the detection means can be any means which
are capable of detecting the state of the user(s) or the using
environment.
The structure of the robot in the first embodiment is not limited
to the one indicated in FIG. 1. For example, the robot can be
incorporated into a stuffed animal or a doll. Further, the use of
the robot described in the first embodiment is not limited, and the
robot can have various uses, e.g., a toy or a device for medical
use.
Further, the drive means in the first embodiment is not limited,
and for example, can be embodied as hands and feet or even a
tail.
In the second embodiment, the driving state of the user is detected
using the throttle sensor, brake sensor, and handle bar sensor, and
based on the detected driving state, the intentions/emotional
expressions are recognized and the pseudo-emotions are generated.
However, the detection means are not limited thereto, and for
example, indication showing a state of the user himself or herself
such as the user's heartbeat or perspiration or facial expressions
are detected, and based on the results, the intentions/emotional
expressions can be recognized and the pseudo-emotions can be
generated. Further, recognition of the intentions/emotional
expressions and generation of the pseudo-emotions can be performed
by using a combination of the state of driving operation and the
state of the user himself or herself.
In addition, the detection means in the third embodiment are not
limited, and any means which are capable of detecting the state of
the user or outdoor environment can be used.
The object to be controlled in the present invention is not
limited, and any objects, as in the first, second, and third
embodiments, such as an outboard engine in a vessel, a robot used
in machine tools, a motor used in electrically-driven vehicles, or
the like can be controlled by adopting the control system of the
present invention based on the same principle as in the aforesaid
embodiments.
It will be understood by those of skill in the art that numerous
variations and modifications can be made without departing from the
spirit of the present invention. Therefore, it should be clearly
understood that the forms of the present invention are illustrative
only and are not intended to limit the scope of the present
invention.
* * * * *